Large survey dataset of rice production practices applied by farmers on their largest farm plot during 2018 in India

This dataset provides detailed information on rice production practices being applied by farmers during 2018 rainy season in India. Data was collected through computer-assisted per-sonal interview of farmers using the digital platform Open Data Kit (ODK). The dataset, n = 8355, covers eight Indian states, viz., Andhra Pradesh, Bihar, Chhattisgarh, Haryana, Odisha, Punjab, Uttar Pradesh and West Bengal. Sampling frames were constructed separately for each district within states and farmers were selected randomly. The survey was deployed in 49 districts with a maximum of 210 interviews per district. The digital survey form was available on mobile phones of trained enumerators and was designed to mini-mize data entry errors. Each survey captured approximately 225 variables around rice production practices of farmers' largest plot starting with land preparation, establishment method, crop variety and planting time through to crop yield. Detailed modules cap-tured fertilizer application, irrigation, weed management, bi-otic and abiotic stresses. Additional information was gath-ered on household demographics and marketing. Geo-points were recorded for each surveyed plot with an accuracy of < 10 m. This dataset is generated to bridge a data-gap in the national system and generates information about the adop-tion of technologies, as well as enabling prediction and other analytics. It can potentially be the basis for evidence-based agriculture programming by policy makers.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

This dataset provides detailed information on rice production practices in India during the 2018 rainy season. The data was collected through personal interviews using the Open Data Kit (ODK) digital platform. The dataset, consisting of 8355 samples, covers eight states in India. This dataset fills a data gap in the national system and can be used as evidence for agriculture programming and policy-making.